Symbol Technologies, LLC

Zebra Technologies Corporation

Microvision, Inc.

Ncr Corporation

Other

At&t Global Information Solutions Company

Duanfeng He

Eugene B Joseph

Mehul M Patel

Bradley S Carlson

Howard M Shepard

Miroslav Trajkovic

Edward D Barkan

Joseph M Katz

Vladimir P Gurevich

Joseph Cai

Kevin D Hunter

Dmitriy Yavid

Mark Correa

Jerome Swartz

Mark J Krichever

Raj Bridgelall

Thomas D Bianculli

Paul R Poloniewicz

Sundeep Kumar

Richard Isaac

Anu Kannan

Warren Zuelch

Mark E Drzymala

Paul Dvorkis

David P Goren

Boris Metlitsky

Chinh Tan

Miklos Stern

Yajun Li

Donald Alexander Collins, Jr

Allan Perry Herrod

Askold Strat

James Robert Fuccello

Dayou Wang

Joel Kahn

David Tsi

Gene A Hofer

Alan J Epshteyn

Michelle X Wang

Joseph D Moreira

Arman Nikzad

Michael Cranston

Peizheng Ma

Donald E Schafer

Ali Tabesh

Steven Kramp

Nong-Qiang Fan

Anatoly Kotlarsky

Igor R Vinogradov

Christopher Warren Brock

David Tsi Shi

Dariusz J Madej

Stephen J Ames

Daniel B Seevers

Thomas Lackemann

William C Sackett

Daniel F Brown

Edward Douglas Hatton

Brad Carlson

Tsi David Shi

Theo Pavlidis

Vincent J Daempfle

Rubin Gelles

Kefei Lu

Thomas M Stankelis

Steven J Shellhammer

William J Callahan

William John Sakoda

Mark Dryzmla

Maulin S Sheth

An indicia location coprocessor is disclosed herein. An example indicia location coprocessor includes imaging device comprising: (1) an image sensor to capture image data; (2) a front-end processing assembly configured to: (i) receive the image data and determine image statistics for the image data, (ii) determine, from the image statistics, an indicia location corresponding to a set of point coordinates in the image data, and, (iii) communicate the image data and the determined indicia location to a host processor communicatively coupled to the front-end processing assembly; and (3) wherein the host processor is configured to receive the image data and the indicia location and is configured to decode an indicia in the image data based upon determining a position in the image data corresponding to the indicia location.

Indicia location coprocessor

Systems and methods for tracking objects in space are disclosed. The systems and methods include capturing two-dimensional (2D) image data from which a barcode is decoded and capturing, generating, or otherwise accessing three-dimensional (3D) image data from which a 3D object is identified. A 2D image of a barcode and barcode data is combined with the 3D object to form reference 3D object data that is used for comparison to subsequently captured 3D and 2D image data. In some examples, a four-dimensional (4D) projection of the reference 3D object data is used for comparison and validation of the subsequently captured 3D and 2D image data.

4D barcode mapping for moving objects

In some implementations, a device may identify, for a printing operation, a media type associated with media involved in the printing operation. The device may receive, from a sensor, a sensor measurement associated with an ambient condition of the printer. The device may determine, using a print optimization model, a printing configuration for the printing operation based on the media type and the ambient condition, wherein the print optimization model is trained based on reference data associated with historical printing operations associated with one or more printers, wherein the reference data includes reference configurations associated with the historical printing operations, respective media types of media used in the historical printing operations, and corresponding ambient conditions of the one or more printers during the historical printing operations. The device may cause the printer to perform the printing operation according to the printing configuration.

Dynamic configuration of a printer for a printing operation

Methods and apparatus to locate and decode an arranged plurality of barcodes in an image are disclosed. An example method includes obtaining image data representing an image of an environment appearing within a FOV of an imaging device that includes the image sensor, wherein an arranged plurality of barcodes appear in the image. A first subset of the plurality of barcodes is decoded from the image data. One or more parameters representing a predicted arrangement of the plurality of barcodes in the image is determined based upon location information associated with each of the decoded first subset of the plurality of barcodes. Possible locations for respective ones of a second subset of the plurality of barcodes are determined based upon the one or more parameters, and the second subset of the plurality of barcodes are attempted to be decoded from the image data in vicinities of the respective possible locations.

Methods and apparatus to locate and decode an arranged plurality of barcodes in an image

Methods and apparatus to locate barcodes in color images are disclosed. An example method includes: obtaining a color digital image using an image sensor, the image including a plurality of pixels represented by respective ones of a plurality of luminance components and respective ones of a plurality of color components; determining, based on the luminance components, a plurality of barcode indicative characteristics for respective ones of a plurality of regions of the image; determining, based on the plurality of color components, a plurality of color content amounts for respective ones of the plurality of regions; identifying one or more regions of the plurality of regions that have their respective color content amount satisfy a first criteria, and their respective barcode indicative characteristic satisfy a second criteria; and processing image data corresponding to each of the one or more regions to attempt to identify one or more barcodes in the image.

Methods and apparatus to locate barcodes in color images

Systems and methods for object recognition with limited input are disclosed herein. An example method includes updating a neural network trained to perform object recognition on a first rendition of an object, so that the neural network performs object recognition on a second rendition of the object, using a limited set of input images. The method includes receiving a limited set of model images of the second rendition of the object, accessing a corresponding image mapping, and generating a large number of training images from the limited set, where image mappings include geometric, illumination, and/or obscuration transformations. The neural network is then trained, from this initial small set, to classify the second rendition of the object.

Object verification/recognition with limited input

A method of generating neural network training image annotations includes training a first neural network to identify and segment hands in images using a first set of 2D images with hand portions segmented in each image; substantially simultaneously capturing both a second set of 2D images, and a third set of images including depth images, depicting hands holding a particular type of object; correlating each of the second set of images with corresponding images of the third set to identify and segment foregrounds from backgrounds in the second set of images; applying the trained first neural network to the identified foregrounds to identify hand portions of the foregrounds and segment object portions from identified hand portions; and training a second neural network, using the segmented object portions of the second set of images as training data, to identify the particular type of object in new images.

Method to generate neural network training image annotations

Example methods and apparatus to read barcodes on reflective surfaces are disclosed herein. An example method for handling a reflective surface in an imaging reader includes capturing one or more images of a surface of an item; detecting whether a specular reflection is present in the one or more images; when a specular reflection is detected, processing one or more additional images of the surface using a predetermined pattern of first processing suitable to identify a barcode on a reflective surface and second processing suitable to identify a barcode on a non-reflective surface; and when a specular reflection is not detected, processing one or more additional images of the surface using the second processing suitable to identify a barcode on a non-reflective surface.

Methods and apparatus to read barcodes on reflective surfaces

A method and apparatus for correcting auto-exposure settings of a barcode reader based on modifying an auto-exposure region at a barcode reader for decoding a barcode in response to identifying a barcode and failing to decode the barcode due to incorrect initial exposure parameters, wherein the modified auto-exposure region is based at least in part on the barcode location.

Auto-exposure region auto-correction

A barcode reader is provided. The barcode reader includes a first image acquisition assembly having a first imager assembly configured to capture infrared (IR) light and an IR illumination assembly configured to emit IR light over at least a portion of a first field of view (FOV) of the first imager assembly so as to illuminate targets within the first FOV. The barcode reader further includes a second image acquisition assembly having a second imager assembly configured to capture visible light and a visible-light illumination assembly configured to emit visible light over at least a portion of a second FOV of the second imager assembly so as to illuminate targets within the second FOV.

Object recognition system with invisible or nearly invisible lighting

A method is disclosed which includes illuminating one or more objects when they enter a field of view (FOV). The method includes capturing an image of one or more objects once they enter the FOV, and performing an iterative analysis of the image based on a plurality of detection operations and according to one or more sequential operational relationships and one or more logical operational relationships. The iterative analysis comprises analyzing the image according to a first detection operation, modifying one or more geometrical operational relationships, and modifying a second detection operation based on the one or more sequential operational relationships, the one or more logical operational relationships, and the one or more geometrical operational relationships. The method further includes repeating the iterative analysis until a final detection operation is completed, and sending a signal to an exterior component to perform an action corresponding to the one or more objects.

Method and system for a machine vision interface

A mobile device captures information from a document. The mobile device selects a template stored in a memory of the mobile device. The selected template describes data content of at least one document. The mobile device then captures an image of a document, determines that the image corresponds to one document described by the selected template by verifying that at least one first region in the image corresponds to a region of the document described by the selected template, identifies at least one region in the image that corresponds with a region in the document described by the selected template, and processes information in the identified region in the image. The mobile device then aggregates application specific information from the processed information and presents the aggregated application specific information to a user program operating on the mobile device.

Method and apparatus for capturing and extracting content from documents on a mobile device

Method and apparatus for capturing and processing content from context sensitive documents on a mobile device

Growing community of inventors

South Setauket, NY, United States of America

Duanfeng He